Identifying Low-head dams (LHD) and creating an inventory become a priority as fatalities continue to occur at these structures. Because obstruction inventories do not specifically identify LHDs, and they are not assigned a hazard classification, there is not an official inventory of LHD. However, there is a multi-agency taskforce that is creating an inventory of LHD. All efforts have been performed by manually identifying LHD on Google Earth Pro (GE Pro). The purpose of this paper is to assess whether a machine learning approach can accelerate the national inventory. We used a machine learning approach to implement a high-resolution remote sensing data and a Convolutional Neural Network (CNN) architecture. The model achieved 76% accuracy on identifying LHD (true positive) and 95% accuracy identifying NLHD (true negative) on the validation set. We deployed the trained model into the National Hydrologic Geospatial Fabric (Hydrofabric) flowlines on the Provo River watershed. The results showed a high number of false positives and low accuracy in identifying LHD due to the mismatch between Hydrofabric flowlines and actual waterways. We recommend improving the accuracy of the Hydrofabric waterway tracing algorithms to increase the percentage of correctly classified LHD.
BYU ScholarsArchive Citation
Vinay Mollinedo, Salvador Augusto, "A Machine Learning Approach for Identification of Low-Head Dams" (2022). Theses and Dissertations. 9813.
low-head dams, machine learning, deep learning, supervised learning, image classification, submerged hydraulic jump, convolutional neural network